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Flood forecasting using support vector machines

机译：使用支持向量机的洪水预报

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This paper describes an application of SVM over the Bird Creek catchment and addresses some important issues in developing and applying SVM in flood forecasting. It has been found that, like artificial neural network models, SVM also suffers from over-fitting and under-fitting problems and the over-fitting is more damaging than under-fitting. This paper illustrates that an optimum selection among a large number of various input combinations and parameters is a real challenge for any modellers in using SVMs. A comparison with some benchmarking models has been made, i.e. Transfer Function, Trend and Naive models. It demonstrates that SVM is able to surpass all of them in the test data series, at the expense of a huge amount of time and effort. Unlike previous published results, this paper shows that linear and nonlinear kernel functions (i.e. RBF) can yield superior performances against each other under different circumstances in the same catchment. The study also shows an interesting result in the SVM response to different rainfall inputs, where lighter rainfalls would generate very different responses to heavier ones, which is a very useful way to reveal the behaviour of a SVM model.
机译：本文介绍了支持向量机在伯德河流域的应用，并讨论了在支持向量机的开发和洪水预报中的一些重要问题。已经发现，像人工神经网络模型一样，SVM也遭受过度拟合和欠拟合的问题，并且过度拟合比欠拟合更具破坏性。本文说明，对于任何使用SVM的建模者来说，在各种输入组合和参数中进行最佳选择是一个真正的挑战。已与一些基准测试模型进行了比较，即传递函数，趋势和朴素模型。它证明了SVM能够在测试数据系列中超越所有这些，而花费了大量的时间和精力。与以前发表的结果不同，本文表明线性和非线性核函数（即RBF）可以在同一集水区的不同情况下相互产生优越的性能。该研究还显示了SVM对不同降雨输入的响应的有趣结果，其中较轻的降雨将对较重的降雨产生非常不同的响应，这是揭示SVM模型行为的非常有用的方法。

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